Plotting with GGPLOT2

#install.packages('ggplot2')
library(ggplot2)

GOALS: Students should be able to use ggplot2 to generate publication quality graphics and understand and use the basics of the grammar of graphics.

• GGPLOT2 developed by Hadley Wickham based on a grammar-of-graphics
• Grammar of graphics consists of a dataset, coordinate system, and geoms – the visual representation of data points.
• Think about figure in layers: like you would in photoshop, illustrator or inkscape.

library(ggplot2)
ggplot(data = gapminder, aes(x = lifeExp, y = gdpPercap)) +
  geom_point()

NOTE:

• First we call the ggplot function -any arguments we provide the ggplot function are considered global options: they apply to all layers
• We passed two arguments to ggplot:
• data
• an aes function - which tells ggplot how variables map to aesthetic properties
• x & y locations

Alone the ggplot call isn’t enough to redner the plot.

ggplot(data = gapminder, aes(x = lifeExp, y = gdpPercap))
## If run, would produce an error. 

Need to tell ggplot how we want to present variables by specifying a geom layer. In the above example we used geom_point to create a scatter plot.

ggplot(data = gapminder, aes(x = lifeExp, y = gdpPercap)) +
  geom_point()

Layers

Using scatter plot not the best way to visualize change over time. Let’s use line plot.

ggplot(data = gapminder, aes(x=year, y=lifeExp, by=country, color=continent)) +
  geom_line()

• used geom_line instead of geom_point

• added a by aesthetic to get a line per country and color by contintent

• visualise both lines and points on the plot?

ggplot(data = gapminder, aes(x=year, y=lifeExp, by=country, color=continent)) +
  geom_line() + geom_point()

• note this is layered: so points have been drawn on top of the lines.
• example of this

ggplot(data = gapminder, aes(x=year, y=lifeExp, by=country)) +
  geom_line(aes(color=continent)) + geom_point()

• the aesthetic mapping of color has been moved from the global plot options in ggplot to the geom_line layer so it no longer applies to the points

Transformations and statistics

• overlay statistical models over the data
• let’s use our first example

ggplot(data = gapminder, aes(x = lifeExp, y = gdpPercap, color=continent)) +
  geom_point()

• hard to see relationships b/t points because of strong outliers in GDP/cap
• change the scale of units on the y axis using the scale functions (controls the mapping between the data values and visual values of an aesthetic)

ggplot(data = gapminder, aes(x = lifeExp, y = gdpPercap)) +
  geom_point() + scale_y_log10()

• log10 function applied a transformation to the values of the gdpPercap column before rendering them on the plot
• each multiple of 10 now only corresponds to an increase in 1 on the transformed scale, e.g. a GDP per capita of 1,000 is now 3 on the y axis, a value of 10,000 corresponds to 4 on the y axis and so on

• This makes it easier to visualise the spread of data on the y-axis.

• We can fit a simple relationship to the data by adding another layer, geom_smooth:

ggplot(data = gapminder, aes(x = lifeExp, y = gdpPercap)) +
  geom_point() + scale_y_log10() + geom_smooth(method="lm")

• make the line thicker by setting the size aesthetic in the geom_smooth layer:

pwd <- ggplot(data = gapminder, aes(x = lifeExp, y = gdpPercap)) +
  geom_point() + scale_y_log10() + geom_smooth(method="lm", size=1.5)

• two ways an aesthetic can be specified:

1. Here we set the size aesthetic by passing it as an argument to geom_smooth.
2. use the aes function to define a mapping between data variables and their visual representation.

Multi-panel figures

• we can split this out over multiple panels by adding a layer of facet panels:

ggplot(data = gapminder, aes(x = year, y = lifeExp, color=continent)) +
  geom_line() + facet_wrap( ~ country)

• facet_wrap layer took a “formula” as its argument, denoted by the tilde (~).
• tells R to draw a panel for each unique value in the country column of the gapminder dataset.

Modifying text

• would like to add text to elements in the graph
• do this by adding a few more layers:
  
• theme layer controls axis text & text size
• scales layer to change legend title

ggplot(data = gapminder, aes(x = year, y = lifeExp, color=continent)) +
  geom_line() + facet_wrap( ~ country) +
  xlab("Year") + ylab("Life expectancy") + ggtitle("Figure 1") +
  scale_fill_discrete(name="Continent") +
  theme(axis.text.x=element_blank(), axis.ticks.x=element_blank())

Resources:

This is just a taste of what you can do with ggplot2. RStudio provides a really useful cheat sheet of the different layers available, and more extensive documentation is available on the ggplot2 website. Finally, if you have no idea how to change something, a quick google search will usually send you to a relevant question and answer on Stack Overflow with reusable code to modify!